New clinical prediction model for early recognition of sepsis in adult primary care patients: a prospective diagnostic cohort study of development and external validation

BACKGROUND: Recognising patients who need immediate hospital treatment for sepsis while simultaneously limiting unnecessary referrals is challenging for GPs. AIM: To develop and validate a sepsis prediction model for adult patients in primary care. DESIGN AND SETTING: This was a prospective cohort study in four out-of-hours primary care services in the Netherlands, conducted between June 2018 and March 2020. METHOD: Adult patients who were acutely ill and received home visits were included. A total of nine clinical variables were selected as candidate predictors, next to the biomarkers C-reactive protein, procalcitonin, and lactate. The primary endpoint was sepsis within 72 hours of inclusion, as established by an expert panel. Multivariable logistic regression with backwards selection was used to design an optimal model with continuous clinical variables. The added value of the biomarkers was evaluated. Subsequently, a simple model using single cut-off points of continuous variables was developed and externally validated in two emergency department populations. RESULTS: A total of 357 patients were included with a median age of 80 years (interquartile range 71-86), of which 151 (42%) were diagnosed with sepsis. A model based on a simple count of one point for each of six variables (aged >65 years; temperature >38°C; systolic blood pressure ≤110 mmHg; heart rate >110/min; saturation ≤95%; and altered mental status) had good discrimination and calibration (C-statistic of 0.80 [95% confidence interval = 0.75 to 0.84]; Brier score 0.175). Biomarkers did not improve the performance of the model and were therefore not included. The model was robust during external validation. CONCLUSION: Based on this study's GP out-of-hours population, a simple model can accurately predict sepsis in acutely ill adult patients using readily available clinical parameters

Creating context for the use of DNA adduct data in cancer risk assessment: II. Overview of methods of identification and quantitation of DNA damage

The formation of deoxyribonucleic acid (DNA) adducts can have important and adverse consequences for cellular and whole organism function. Available methods for identification of DNA damage and quantification of adducts are reviewed. Analyses can be performed on various samples including tissues, isolated cells, and intact or hydrolyzed (digested) DNA from a variety of biological samples of interest for monitoring in humans. Sensitivity and specificity are considered key factors for selecting the type of method for assessing DNA perturbation. The amount of DNA needed for analysis is dependent upon the method and ranges widely, from 14C- and 3H-) binding, 32P-postlabeling, and methods dependent on gas chromatography (GC) or high-performance liquid chromatography (HPLC) with detection by electron capture, electrochemical detection, single or tandem mass spectrometry, or accelerator mass spectrometry. Sensitivity is ranked, and ranges from ~1 adduct in 104 to 1012 nucleotides. A brief overview of oxidatively generated DNA damage is also presented. Assay limitations are discussed along with issues that may have impact on the reliability of results, such as sample collection, processing, and storage. Although certain methodologies are mature, improving technology will continue to enhance the specificity and sensitivity of adduct analysis. Because limited guidance and recommendations exist for adduct analysis, this effort supports the HESI Committee goal of developing a framework for use of DNA adduct data in risk assessment